NOAA STAR produces two lines of gridded 0.02deg super-collated L3S LEO datasets from Low Earth Orbiting (LEO) satellites, one from the NOAA afternoon JPSS (L3S_LEO_PM) and the other from the EUMETSAT mid-morning Metop-FG (L3S_LEO_AM). The L3S_LEO_PM is derived from JPSS satellites (in v2.80, NPP and N20) with VIIRS sensor onboard (0.75km/nadir). The L3S_LEO_PM dataset is produced by aggregating L3U datasets from two JPSS satellites ( https://doi.org/10.5067/GHVRS-3UO28 and https://doi.org/10.5067/GHV20-3UO28 ) and covers from Feb 2012-present. The L3S-LEO-PM data are reported in two files per 24hr interval, one daytime and one nighttime (nominal JPSS local equator crossing times around 01:30/13:30). Data is in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). The Near-Real Time (NRT) L3S-LEO data are archived at PO.DAAC with approximately 6 hours latency and then replaced by the Delayed Mode files about 2 months later, with identical file names. In addition to SST, the L3S-LEO files report the location and intensity of thermal fronts. The NRT/DM data are seamlessly stitched with the full-mission Reanalysis (RAN). The ACSPO L3S products are monitored and validated against in situ data in the NOAA iQuam system ( https://www.star.nesdis.noaa.gov/socd/sst/iquam ) in the NOAA SQUAM system ( https://www.star.nesdis.noaa.gov/socd/sst/squam ). Quality of SST imagery, clear-sky mask and thermal fronts is evaluated in the NOAA ARMS system ( https://www.star.nesdis.noaa.gov/socd/sst/arms ). NOAA plans to include data from other afternoon platforms and sensors, such as N21 and Aqua MODIS, into the future releases of the L3S_LEO_PM.

This map presents all layers corresponding to "Camping grounds, recreational vehicle parks and trailer parks" activities in the Atlantic area. For more information about this NACE code : https://ec.europa.eu/eurostat/ramon/nomenclatures/index.cfm?TargetUrl=DSP_NOM_DTL_VIEW&StrNom=NACE_REV2&StrLanguageCode=EN&IntPcKey=18513734&IntKey=18513764&StrLayoutCode=HIERARCHIC&IntCurrentPage=1 Indicators collected are : Number of places per NUTS 3 unit of the Atlantic Area

Key physico-chemical parameters (salinity, temperature, turbidity and dissolved oxygen) were measured in surface water during longitudinal transects in the Loire and Gironde estuaries in summers 2017 and 2018. This objective of this work was to determine the distribution of the dissolved oxygen and to detect potential severe desoxygenation. The transects were scheduled in order to begin the measurements at high tide from a site located upstream of an area where severe deoxygenation have been already been reported. Then, the transect was realised by sailing at low speed downstream with a multiparameter probe SAMBAT, maintained at 0.5 m below the surface, that collected a measurement every 2 minutes.

NOAA produces two lines of gridded 0.02deg super-collated L3S LEO SST datasets from Low Earth Orbiting (LEO) satellites, one from the NOAA afternoon JPSS (L3S_LEO_PM) and the other from the EUMETSAT mid-morning Metop-FG (L3S_LEO_AM). The L3S_LEO_AM is derived from Metop-A, -B and -C. The Metop-FG satellite program was jointly established by ESA and EUMETSAT. The US NOAA, under the Initial Joint Polar System Agreement with EUMETSAT, has contributed three AVHRR sensors capable of collecting and transmitting data in the Full Resolution Area Coverage (FRAC; 1km/nadir) format. The L3S_LEO_AM dataset is produced by aggregating three L3U datasets from MetOp-FG satellites ( http://doi.org/10.5067/GHMTA-3US28 , http://doi.org/10.5067/GHMTB-3US28 , http://doi.org/10.5067/GHMTC-3US28 ) and covers from Dec 2006-present. The L3S-LEO-AM data are reported in two files per 24hr interval, one daytime and one nighttime (nominal Metop local equator crossing times around 09:30/21:30, respectively), in NetCDF4 format, compliant with the GHRSST Data Specification version 2 (GDS2). The Near-Real Time (NRT) L3S-LEO data are archived at PO.DAAC with approximately 6 hours latency and then replaced by the Delayed Mode files about 2 months later, with identical file names. The NRT/DM data are seamlessly stitched with the full-mission Reanalysis (RAN). In addition to SST, the L3S-LEO files report the location and intensity of thermal fronts. The ACSPO L3S products are monitored and validated against in situ data in the NOAA iQuam system ( https://www.star.nesdis.noaa.gov/socd/sst/iquam ) in the NOAA SQUAM system ( https://www.star.nesdis.noaa.gov/socd/sst/squam ). Quality of SST imagery and clear-sky mask is evaluated in the NOAA ARMS system ( https://www.star.nesdis.noaa.gov/socd/sst/arms ). NOAA plans to include data from other mid-morning platforms and sensors, such as Metop-SG METImage, into L3S_LEO_AM.

Seasonal Climatology of Phosphate for Loire River for the period 1964-2019 and for the following seasons: - winter: January-March, - spring: April-June, - summer: July-September, - autumn: October-December Observational data span from 1964 to 2019. Depth levels (m): -125.0, -100.0, -75.0, -50.0,-40.0, -30.0, -25.0, -20.0, -15.0, -10.0, -8.0, -6.0, -4.0, -2.0, -0.0 Data Sources: observational data from SeaDataNet/EMODNet Chemistry Data Network. Description of DIVAnd analysis: The computation was done with DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.7.4, using GEBCO 30sec topography for the spatial connectivity of water masses. The horizontal resolution of the produced DIVAnd maps grids is 0.01 degrees. Correlation length was optimized and filtered vertically and a seasonally-averaged profile was used. Signal to noise ratio was fixed to 1. Logarithmic transformation applied to the data prior to the analysis. Background field: the data mean value is subtracted from the data. The weight of time series was decreased by a factor of 10 relative to the weight of the profiles to account for the redundancy in the time series observations. Detrending of data: no, Advection constraint applied: no. Units: umol/l.

Seasonal Climatology of Dissolved Inorganic Nitrogen (DIN) for Loire River for the period 1990-2019 and for the following seasons: - winter: January-March, - spring: April-June, - summer: July-September, - autumn: October-December Observational data span from 1990 to 2019. Depth levels (m): -125.0, -100.0, -75.0, -50.0,-40.0, -30.0, -25.0, -20.0, -15.0, -10.0, -8.0, -6.0, -4.0, -2.0, -0.0 Data Sources: observational data from SeaDataNet/EMODNet Chemistry Data Network. Description of DIVAnd analysis: The computation was done with DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.7.4, using GEBCO 30sec topography for the spatial connectivity of water masses. The horizontal resolution of the produced DIVAnd maps grids is 0.01 degrees. Correlation length was optimized and filtered vertically and a seasonally-averaged profile was used. Signal to noise ratio was fixed to 1 for vertical profiles and to 0.1 for time series to account for the redundancy in the time series observations. Logarithmic transformation applied to the data prior to the analysis. Background field: the data mean value is subtracted from the data. . Detrending of data: no, Advection constraint applied: no. Units: umol/l.

This metadata refers to the EEA marine assessment grid, to which all data and assessment results have been spatially mapped in order to ensure that data can be compared in a uniform way across the European regional seas. The marine assessment grid is based on the EEA reference grid system. The EEA reference grid is based on ERTS89 Lambert Azimuthal Equal Area projection with parameters: latitude of origin 52° N, longitude of origin 10° E, false northing 3 210 000.0 m, false easting 4 321 000.0 m. All grid cells are named with a unique identifier containing information on grid cell size and the distance from origin in meters (easting and northing). An important attribute of the EEA reference grid system is that by using an equal area projection all grid cells are having the same area for the same grid size. In this marine assessment grid, two grid sizes are used: * 100 x 100 km in offshore areas (> 20 km from the coastline) * 20 x 20 km in coastal areas (<= 20 km from the coastline) The grid sizes were choosen after an evaluation of data availability versus the need for sufficient detail in the resulting assessment. The resulting assessment grid is a combination of two grid sizes using the EEA reference grid system. The overall area of interest used in the grid is based on the marine regions and subregions under the Marine Strategy Framework Directive (MSFD). Additionally, Norwegian (Barent Sea and Norwegian Sea) and Icelandic waters (’Iceland Sea’) have been added (see Surrounding seas of Europe). Note that, within the North East Atlantic region, only the subregions within EEZ boundaries (~200 nm) have been included.

This map presents all layers corresponding to "Operation of sports facilities" activities in the Atlantic area. For more information about this NACE code : https://ec.europa.eu/eurostat/ramon/nomenclatures/index.cfm?TargetUrl=DSP_NOM_DTL_VIEW&StrNom=NACE_REV2&StrLanguageCode=EN&IntPcKey=18522104&IntKey=18522134&StrLayoutCode=HIERARCHIC&IntCurrentPage=1 Indicators collected are : - Number of persons employed and number of employees in full time equivalent units per NUTS 3 unit of the Atlantic Area - Number of nautical sport facilities per NUTS3 unit of the Atlantic Area

''' Short description: ''' For the Mediterranean Sea - the CNR diurnal sub-skin Sea Surface Temperature (SST) product provides daily gap-free (L4) maps of hourly mean sub-skin SST at 1/16° (0.0625°) horizontal resolution over the CMEMS Mediterranean Sea (MED) domain, by combining infrared satellite and model data (Marullo et al., 2014). The implementation of this product takes advantage of the consolidated operational SST processing chains that provide daily mean SST fields over the same basin (Buongiorno Nardelli et al., 2013). The sub-skin temperature is the temperature at the base of the thermal skin layer and it is equivalent to the foundation SST at night, but during daytime it can be significantly different under favorable (clear sky and low wind) diurnal warming conditions. The sub-skin SST L4 product is created by combining geostationary satellite observations aquired from SEVIRI and model data (used as first-guess) aquired from the CMEMS MED Monitoring Forecasting Center (MFC). This approach takes advantage of geostationary satellite observations as the input signal source to produce hourly gap-free SST fields using model analyses as first-guess. The resulting SST anomaly field (satellite-model) is free, or nearly free, of any diurnal cycle, thus allowing to interpolate SST anomalies using satellite data acquired at different times of the day (Marullo et al., 2014). [https://help.marine.copernicus.eu/en/articles/4444611-how-to-cite-or-reference-copernicus-marine-products-and-services How to cite] '''DOI (product) :''' https://doi.org/10.48670/moi-00170

Satellite altimeters routinely supply sea surface height (SSH) measurements which are key observations to monitor ocean dynamics. However, below a wavelength of about 70 km, along-track altimeter measurements are often characterized by a dramatic drop in the signal-to-noise ratio, making it very challenging to fully exploit available altimeter observations to precisely analyze small mesoscale variations in SSH. Although various approaches have been proposed and applied to identify and filter noise from measurements, no distinctive methodology emerged to be systematically applied in operational products. To best cope with this unresolved issue, the Copernicus Marine Environment Monitoring Service (CMEMS) actually provides simple band-pass filtered data to mitigate noise contamination in the along-track SSH signals and more innovative and adapted noise filtering methods are thus left to users seeking to unveil small-scale altimeter signals. Here demonstrated, a fully data-driven approach is developed and applied to provide robust estimates of noise-free Sea Level Anomaly (SLA) signals. The method combines Empirical Mode Decomposition (EMD), to help analyze non-stationary and non-linear processes, and an adaptive noise filtering technique inspired by Discrete Wavelet Transform (DWT) decompositions. It is now found to best resolve the distribution of the sea surface height variability in the mesoscale 30-120 km wavelength band. A practical uncertainty variable is attached to the denoised SLA estimates that accounts for errors related to the local signal to noise ratio, but also for uncertainties in the denoising process, which assumes that SLA variability results in part from a stochastic process. Here, measurements from the Jason-3, Sentinel-3 A and SARAL/AltiKa altimeters are processed and analyzed, and their energy spectral and seasonal distributions characterized in the small mesoscale domain. Anticipating data from the upcoming Surface Water and Ocean Topography (SWOT) mission, these denoised SLA measurements for three reference altimeter missions already yield valuable opportunities to assess global small mesoscale kinetic energy distributions. This dataset was developed within the Ocean Surface Topography Science Team (OSTST) activities. A grant was awarded to the SASSA (Satellite Altimeter Short-scale Signals Analysis) project by the TOSCA board in the framework of the CNES/EUMETSAT call CNES-DSP/OT 12-2118. Altimeter data were provided by the Copernicus Marine Environment Monitoring Service (CMEMS) and by the Sea State Climate Change Initiative (CCI) project.